Hi,
I have implemented some approximations for studentized range quantiles and
probabilities based on John R. Gleason's (1999) "An accurate, non-iterative
approximation for studentized range quantiles." Computational Statistics &
Data Analysis, (31), 147-158.
Probability approximations rely on scipy.optimize.fminbound. The functions
accept both scalars or array-like data thanks to numpy.vectorize. A fair
amount of validation and testing has been conducted on the code. More
details can be found here: http://code.google.com/p/qsturng-py/
I welcome any thoughts as to whether you all think this might be useful to
add to SciPy or make into a scikit. Any general comments would be helpful as
well. I should mention I'm a cognitive neuroscientist by trade, my use of
statistical jargon probably isn't that good.
Regards,
Roger
Roger Lew
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